README.md

metadata

language:
  - en
size_categories:
  - 100K<n<1M
task_categories:
  - text-classification
pretty_name: arXiv category classification data
dataset_info:
  - config_name: arxiv_category_descriptions
    features:
      - name: tag
        dtype: string
      - name: name
        dtype: string
      - name: description
        dtype: string
    splits:
      - name: arxiv_category_descriptions
        num_bytes: 54944
        num_examples: 157
    download_size: 29500
    dataset_size: 54944
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: title
        dtype: string
      - name: abstract
        dtype: string
      - name: categories
        sequence: string
      - name: creation_date
        dtype: timestamp[ns, tz=UTC]
    splits:
      - name: train
        num_bytes: 177097626
        num_examples: 163168
      - name: validation
        num_bytes: 22135139
        num_examples: 20396
      - name: test
        num_bytes: 22157669
        num_examples: 20397
    download_size: 126803256
    dataset_size: 221390434
configs:
  - config_name: arxiv_category_descriptions
    data_files:
      - split: arxiv_category_descriptions
        path: arxiv_category_descriptions/arxiv_category_descriptions-*
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: mit
tags:
  - science
  - scholarly

📄 Paper: Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes (ICNLSP 2024)

💻 GitHub: https://github.com/sebischair/FusionSent

This is a dataset of scientific documents derived from arXiv metadata. The arXiv metadata provides information about more than 2 million scholarly articles published in arXiv from various scientific fields. We use this metadata to create a dataset of 203,961 titles and abstracts categorized into 130 different classes. To this end, we first perform stratified downsampling of the metadata to only 10% of all articles while retaining the original class distribution. Afterward, articles assigned to categories occurring less than 100 times in the downsampled dataset are removed. To obtain the final dataset, we then perform a stratified train/validation/test split of the processed dataset in an 80:10:10 ratio. The number of examples in each set is shown in the table below.

• The default subset contains the dataset with the document categories as classes in the form of lists of strings. The categories are ordered hierarchically according to the arXiv category taxonomy. In this dataset, the -> symbols indicate a parent->child relationship between categories that can be linked and create a path from the root to the leaf node. For classification, you can either use the complete paths as classes or just parse the respective leaf nodes as classes, resulting in the same (abbreviated) categories.
• The arxiv_category_descriptions subset contains the tags, names, and textual descriptions of the leaf nodes from the arXiv category taxonomy.

Split	Number of Samples
Train	163,168
Validation	20,396
Test	20,397

Each article in the resulting arXiv dataset is categorized into one or more distinct categories. The figure below shows the distribution of papers across the 130 categories of the dataset.

License

MIT

Citation information

When citing our work in academic papers and theses, please use this BibTeX entry:

@inproceedings{schopf-etal-2024-efficient,
    title = "Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes",
    author = "Schopf, Tim  and
      Blatzheim, Alexander  and
      Machner, Nektarios  and
      Matthes, Florian",
    editor = "Abbas, Mourad  and
      Freihat, Abed Alhakim",
    booktitle = "Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024)",
    month = oct,
    year = "2024",
    address = "Trento",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.icnlsp-1.21",
    pages = "186--198",
}